Journal of endourology最新文献

{"title":"Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.","authors":"Daniele Amparore, Sabrina De Cillis, Eugenio Alladio, Michele Sica, Federico Piramide, Paolo Verri, Enrico Checcucci, Alberto Piana, Alberto Quarà, Edoardo Cisero, Matteo Manfredi, Michele Di Dio, Cristian Fiori, Francesco Porpiglia","doi":"10.1089/end.2024.0057","DOIUrl":"10.1089/end.2024.0057","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. <b><i>Materials and Methods:</i></b> In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. <b><i>Results:</i></b> A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. <b><i>Conclusions:</i></b> Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140184609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objective Evaluation of Gaze Location Patterns Using Eye Tracking During Cystoscopy and Artificial Intelligence-Assisted Lesion Detection.","authors":"Atsushi Ikeda, Kazuya Izumi, Kensuke Katori, Hirokazu Nosato, Keita Kobayashi, Shuhei Suzuki, Shuya Kandori, Masaru Sanuki, Yoichi Ochiai, Hiroyuki Nishiyama","doi":"10.1089/end.2023.0699","DOIUrl":"10.1089/end.2023.0699","url":null,"abstract":"<p><p><b><i>Background:</i></b> The diagnostic accuracy of cystoscopy varies according to the knowledge and experience of the performing physician. In this study, we evaluated the difference in cystoscopic gaze location patterns between medical students and urologists and assessed the differences in their eye movements when simultaneously observing conventional cystoscopic images and images with lesions detected by artificial intelligence (AI). <b><i>Methodology:</i></b> Eye-tracking measurements were performed, and observation patterns of participants (24 medical students and 10 urologists) viewing images from routine cystoscopic videos were analyzed. The cystoscopic video was captured preoperatively in a case of initial-onset noninvasive bladder cancer with three low-lying papillary tumors in the posterior, anterior, and neck areas (urothelial carcinoma, high grade, and pTa). The viewpoint coordinates and stop times during observation were obtained using a noncontact type of gaze tracking and gaze measurement system for screen-based gaze tracking. In addition, observation patterns of medical students and urologists during parallel observation of conventional cystoscopic videos and AI-assisted lesion detection videos were compared. <b><i>Results:</i></b> Compared with medical students, urologists exhibited a significantly higher degree of stationary gaze entropy when viewing cystoscopic images (<i>p</i> < 0.05), suggesting that urologists with expertise in identifying lesions efficiently observed a broader range of bladder mucosal surfaces on the screen, presumably with the conscious intent of identifying pathologic changes. When the participants observed conventional and AI-assisted lesion detection images side by side, contrary to urologists, medical students showed a higher proportion of attention directed toward AI-detected lesion images. <b><i>Conclusion:</i></b> Eye-tracking measurements during cystoscopic image assessment revealed that experienced specialists efficiently observed a wide range of video screens during cystoscopy. In addition, this study revealed how lesion images detected by AI are viewed. Observation patterns of observers' gaze may have implications for assessing and improving proficiency and serving educational purposes. To the best of our knowledge, this is the first study to utilize eye tracking in cystoscopy. University of Tsukuba Hospital, clinical research reference number R02-122.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140207063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Endourology: Maximizing the Promise Through Consideration of the Principles of Diffusion of Innovation Theory.","authors":"Manoj Monga, Natalie C Edwards, Sirikan Rojanasarot, Mital Patel, Erin Turner, Jeni White, Samir Bhattacharyya","doi":"10.1089/end.2023.0680","DOIUrl":"10.1089/end.2023.0680","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Diffusion of Innovation Theory explains how ideas or products gain momentum and diffuse (or spread) through specific populations or social systems over time. The theory analyzes primary influencers of the spread of new ideas, including the innovation itself, communication channels, time, and social systems. <b><i>Methods:</i></b> The current study reviewed published medical literature to identify studies and applications of artificial intelligence (AI) in endourology and used E.M. Rogers' Diffusion of Innovation Theory to analyze the primary influencers of the adoption of AI in endourological care. The insights gained were triaged and prioritized into AI application-related action items or \"tips\" for facilitating the appropriate diffusion of the most valuable endourological innovations. <b><i>Results:</i></b> Published medical literature indicates that AI is still a research-based tool in endourology and is not widely used in clinical practice. The published studies have presented AI models and algorithms to assist with stone disease detection (<i>n</i> = 17), the prediction of management outcomes (<i>n</i> = 18), the optimization of operative procedures (<i>n</i> = 9), and the elucidation of stone disease chemistry and composition (<i>n</i> = 24). Five tips for facilitating appropriate adoption of endourological AI are: (1) Develop/prioritize training programs to establish the foundation for effective use; (2) create appropriate data infrastructure for implementation, including its maintenance and evolution over time; (3) deliver AI transparency to gain the trust of endourology stakeholders; (4) adopt innovations in the context of continuous quality improvement Plan-Do-Study-Act cycles as these approaches have proven track records for improving care quality; and (5) be realistic about what AI can/cannot currently do and document to establish the basis for shared understanding. <b><i>Conclusion:</i></b> Diffusion of Innovation Theory provides a framework for analyzing the influencers of the adoption of AI in endourological care. The five tips identified through this research may be used to facilitate appropriate diffusion of the most valuable endourological innovations.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging.","authors":"Labeeqa Khizir, Vineet Bhandari, Srivarsha Kaloth, John Pfail, Benjamin Lichtbroun, Naveena Yanamala, Sammy E Elsamra","doi":"10.1089/end.2023.0695","DOIUrl":"10.1089/end.2023.0695","url":null,"abstract":"<p><p>The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities, such as X-rays, CT, and MRI, to improve image quality and generate high-resolution three-dimensional images. AI reconstruction of three-dimensional models of patient anatomy from CT or MRI scans can better enable urologists to visualize structures and accurately plan surgical approaches. AI can also be optimized to create virtual reality simulations of surgical procedures based on patient-specific data, giving urologists more hands-on experience and preparation. Recent development of artificial intelligence modalities, such as TeraRecon and Ceevra, offer rapid and efficient medical imaging analyses aimed at enhancing the provision of urologic care, notably for intraoperative guidance during robot-assisted radical prostatectomy (RARP) and partial nephrectomy.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study.","authors":"Runzhuo Ma, Dani Kiyasseh, Jasper A Laca, Rafal Kocielnik, Elyssa Y Wong, Timothy N Chu, Steven Cen, Cherine H Yang, Istabraq S Dalieh, Taseen F Haque, Mitch G Goldenberg, Xiuzhen Huang, Anima Anandkumar, Andrew J Hung","doi":"10.1089/end.2023.0328","DOIUrl":"10.1089/end.2023.0328","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial intelligence (AI)-based feedback on a robotic suturing task. <b><i>Materials and Methods:</i></b> Forty-two participants with no robotic surgical experience were randomized to a control or feedback group and video-recorded while completing two rounds (R1 and R2) of suturing tasks on a da Vinci surgical robot. Participants were assessed on needle handling and needle driving, and feedback was provided via a visual interface after R1. For feedback group, participants were informed of their AI-based skill assessment and presented with specific video clips from R1. For control group, participants were presented with randomly selected video clips from R1 as a placebo. Participants from each group were further labeled as underperformers or innate-performers based on a median split of their technical skill scores from R1. <b><i>Results:</i></b> Demographic features were similar between the control (<i>n</i> = 20) and feedback group (<i>n</i> = 22) (<i>p</i> > 0.05). Observing the improvement from R1 to R2, the feedback group had a significantly larger improvement in needle handling score (0.30 <i>vs</i> -0.02, <i>p</i> = 0.018) when compared with the control group, although the improvement of needle driving score was not significant when compared with the control group (0.17 <i>vs</i> -0.40, <i>p</i> = 0.074). All innate-performers exhibited similar improvements across rounds, regardless of feedback (<i>p</i> > 0.05). In contrast, underperformers in the feedback group improved more than the control group in needle handling (<i>p</i> = 0.02). <b><i>Conclusion:</i></b> AI-based feedback facilitates surgical trainees' acquisition of robotic technical skills, especially underperformers. Future research will extend AI-based feedback to additional suturing skills, surgical tasks, and experience groups.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71412478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using ChatGPT-4 to Analyze 24-Hour Urine Results and Generate Custom Dietary Recommendations for Nephrolithiasis.","authors":"Satomi Kiriakedis, Brian Duty, Tyler Chase, Raghav Wusirika, Ian Metzler","doi":"10.1089/end.2024.0055","DOIUrl":"10.1089/end.2024.0055","url":null,"abstract":"<p><p><b><i>Purpose:</i></b> The increasing incidence of nephrolithiasis underscores the need for effective, accessible tools to aid urologists in preventing recurrence. Despite dietary modification's crucial role in prevention, targeted dietary counseling using 24-hour urine collections is underutilized. This study evaluates ChatGPT-4, a multimodal large language model, in analyzing urine collection results and providing custom dietary advice, exploring the potential for artificial intelligence-assisted analysis and counseling. <b><i>Materials and Methods:</i></b> Eleven unique prompts with synthesized 24-hour urine collection results were submitted to ChatGPT-4. The model was instructed to provide five dietary recommendations in response to the results. One prompt contained all \"normal\" values, with subsequent prompts introducing one abnormality each. Generated responses were assessed for accuracy, completeness, and appropriateness by two urologists, a nephrologist, and a clinical dietitian. <b><i>Results:</i></b> ChatGPT-4 achieved average scores of 5.2/6 for accuracy, 2.4/3 for completeness, and 2.6/3 for appropriateness. It correctly identified all \"normal\" values but had difficulty consistently detecting abnormalities and formulating appropriate recommendations. The model performed particularly poorly in response to calcium and citrate abnormalities and failed to address 3/10 abnormalities entirely. <b><i>Conclusions:</i></b> ChatGPT-4 exhibits potential in the dietary management of nephrolithiasis but requires further refinement for dependable performance. The model demonstrated the ability to generate personalized recommendations that were often accurate and complete but displayed inconsistencies in identifying and addressing urine abnormalities. Despite these limitations, with precise prompt design, physician oversight, and continued training, ChatGPT-4 can serve as a foundation for personalized medicine while also reducing administrative burden, indicating its promising role in improving the management of conditions such as nephrolithiasis.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140891923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.","authors":"Catherine Sánchez, Francisca Larenas, Juan Sebastián Arroyave, Christopher Connors, Belén Giménez, Michael A Palese, Juan Fulla","doi":"10.1089/end.2023.0702","DOIUrl":"10.1089/end.2023.0702","url":null,"abstract":"<p><p>This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered around patient-specific characteristics, lifestyle considerations, and diet have been limited. Our study comprised a robust sample size of 976 Chilean participants, with meticulously analyzed demographic, lifestyle, and health data through a comprehensive questionnaire. We developed a predictive model using various classifiers, including logistic regression, decision trees, random forests, and extra trees, reaching high accuracy (88%) in identifying individuals at risk of kidney stone formation. Key protective factors highlighted by the algorithm include the pivotal role of hydration, physical activity, and dietary patterns that played a crucial role, emphasizing the protective nature of higher fruit and vegetable intake, balanced dairy consumption, and the nuanced impact of specific protein sources on kidney stone risk. In contrast, identified risk factors encompassed gender disparities with males found to be 2.31 times more likely to develop kidney stones than females. Thirst and self-perceived dark urine color emerged as strong predictors, with a significant increase in the likelihood of stone formation. The development of predictive tools with AI, in urolithiasis management signifies a paradigm shift toward more precise and personalized health care. The algorithm's ability to process extensive datasets, including dietary habits, heralds a new era of data-driven medical practice. This research underscores the transformative impact of AI in medical diagnostics and prevention, paving the way for a future where health care interventions are not only more effective but also tailored to individual patient needs. In this case, AI is an important tool that can help patients stay healthy, prevent diseases, and make informed decisions about their overall well-being.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacological Modulation of Intrarenal Pressure in a Porcine Model Using a Novel Isoprenaline-Eluting Guidewire.","authors":"Jeff John, Mark Wellman, Tracy Kellermann, Kamil Kopeć, Tomasz Ciach, Graham Fieggen, Lisa Kaestner, John Lazarus","doi":"10.1089/end.2024.0348","DOIUrl":"10.1089/end.2024.0348","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Several complications of retrograde intrarenal surgery have been attributed to inadvertent increases in intrarenal pressure. We recently described the development of an innovative isoprenaline-eluting guidewire (IsoWire). The objective of this study was to investigate the impact of this IsoWire on the intrarenal pressure and evaluate its safety. <b><i>Materials and Methods:</i></b> This study was performed in 17 renal units using a porcine model. As controls, the intrarenal pressure, heart rate, and mean arterial pressure were measured for a duration of six minutes with a standard guidewire placed in the renal pelvis. For the experiment, the conventional guidewire was substituted with the IsoWire and the same parameters were measured. Blood samples were taken at one-minute intervals to measure plasma isoprenaline levels. This procedure was repeated on the opposite side. <b><i>Results:</i></b> The mean intrarenal pressure reduction was 29% (95% CI: 13%-53%). The mean isoprenaline effect time was 174 seconds. No changes in heart rate (<i>p</i> = .908) or mean arterial pressure (<i>p</i> = .749) were recorded after IsoWire insertion. Plasma isoprenaline levels were below the quantitation threshold. Isoprenaline concentrations in the plasma were below the quantification threshold. Ureteroscopy revealed no ureteral lesions. <b><i>Conclusions:</i></b> The IsoWire demonstrated a safe and effective reduction of intrarenal pressure. Additional research is necessary to determine whether ureteral smooth muscle relaxation generated by isoprenaline facilitates easier insertion of a ureteral access sheath, decreases the incidence of ureteral access sheath related ureteral lesions, or even encourage the practice of sheathless retrograde intrarenal surgery.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renal Access in Pediatric Supine Miniaturized Percutaneous Nephrolithotomy: Comparative Evaluation of Ultrasound-Fluoroscopy Combined and Biplanar (0°-90°) Fluoroscopic Techniques.","authors":"Ali Sezer, Bilge Türedi, Onur Kucuktopcu, Mustafa Bilal Hamarat, Burak Yilmaz, Rasim Güzel, Kemal Sarica","doi":"10.1089/end.2024.0181","DOIUrl":"10.1089/end.2024.0181","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Ultrasound (US)-guided puncture has the benefits of avoiding radiation and limiting the risk of visceral injury. We aimed to evaluate the results of two different renal access techniques during pediatric supine mini percutaneous nephrolithotomy (smPCNL) in a comparative manner. <b><i>Patients and Methods:</i></b> Data obtained from pediatric patients undergoing smPCNL by single surgeon between September 2021 and 2023 were reviewed retrospectively. Children were divided into two groups namely; biplanar 0°-90° fluoroscopy (Group-F) and US-fluoroscopy combined (Group-C). In all cases, preoperative, operative, and postoperative findings were recorded. Success was defined as the determination of either no (complete stone-free status) or < 4 mm residual fragments (CIRF) on US and X-ray (postoperative 3rd month) images. Complications were evaluated according to modified Clavien-Dindo classification. <b><i>Results:</i></b> Data of 54 patients with a mean age of 8.6 years (Group-F = 30, Group-C = 24) are reviewed. In addition to the similar success rates in both groups (Group-F = 86.7% Group-C = 87.5% <i>p</i> = 0.928), similar minor complications were noted in the majority of the cases. No child required transfusion and/or angioembolization. Although the fluoroscopy and operation time were lower in Group-C, the difference was not statistically significant. <b><i>Conclusion:</i></b> US-fluoroscopy combined access technique can be applied with similar success and complication rates in pediatric smPCNL. Ultimately, as experience is gained, this technique may lower radiation exposure, although this was not observed in the current study.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply Letter to Dr Victor DR et al. on: Preoperative α1-Blockers Impact on Outcomes of Patients Undergoing Ureteroscopy with Ureteral Access Sheaths: A Systematic Review and Meta-Analysis.","authors":"Guglielmo Mantica, Giovanni Drocchi, Carlo Terrone","doi":"10.1089/end.2024.0455","DOIUrl":"10.1089/end.2024.0455","url":null,"abstract":"","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}